The performance of artificial networks is proved to be improving by artificial astrocytes, which scales the recent finding of direct involvement of astrocytes in brain information processing. The improvement provided by artificial astrocytes increases as the network complexity increases, which agrees with the gradual increase of the glia proportion observed in the phylogeny as the nervous system complexity increases. The following study results are published in the below article and detail of the paper are made available below the post.

This study could be a cornerstone in the understanding of astrocyte-neuron communication and does support the current understanding that neuron-glia interaction in biological synapses represents a fine tuning in communication process. As per the authors, once this proof of concept is established, the development of future models of astrocyte-neuron interaction that incorporate the richness of biological interactions, e.g., astrocyte-induced synaptic depression, or depression and potentiation altogether, as well as spatial spread of the astrocyte signaling and astrocyte-astrocyte communication, are required to test whether they provide similar, or even better, improvements of neural network performances. Likewise, future work is necessary to investigate the impact of astrocytes in more complex neural networks that include e.g., inhibitory neurons and/or feed-back neuronal communication. It’s good article to read and I recommend the interested people or in the domain to have a read.

Article Abstract : Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.